Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors.

BACKGROUND: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP). METHODS: The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects. RESULTS: At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized ß ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. CONCLUSION: In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health. TRIAL REGISTRATION: ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.

Association between frailty and sleep quality in people living with multiple sclerosis and obesity: An observational cross-sectional study.

BACKGROUND: A majority of the people with multiple sclerosis (pwMS) experience sleep disturbances. Frailty is also common in pwMS. The geriatric literature strongly suggests that frailty is associated with worse sleep outcomes in community-dwelling older adults, but this association has yet to be explored among pwMS. This study focused on examining the association between frailty and sleep quality in pwMS. METHODS: Seventy-six people with both MS and obesity (mean age: 47.6 ± 10.9 years, 81.6 % female, mean body mass index (BMI): 37.10 ± 5.5 kg/m2, mean Patient Determined Disease Steps (PDDS): 0.82 ± 1.20) were included in this cross-sectional secondary analysis. A comprehensive frailty index (FI) based on 41 health deficits from various health domains was calculated based on standardized procedures. Sleep quality was determined by the Pittsburgh Sleep Quality Index questionnaire (PSQI). RESULTS: Overall, 67.1 % of the participants were identified as non-frail (FI ≤ 0.25), and 32.9 % were identified as frail (FI > 0.25). A significant correlation was observed between FI scores and global PSQI scores (ρ = 0.43, p < 0.05). Cross-tabulation analyses revealed that frail participants had worse subjective sleep quality, sleep latency, habitual sleep efficiency, sleep disturbances, daytime dysfunction, and higher use of sleep medications compared to non-frail participants (p < 0.05). CONCLUSIONS: The current study identified a significant association between frailty and sleep quality in people with both MS and obesity with minimal disability. These findings underscore the importance of untangling the relationship between frailty and sleep quality in pwMS. These results could lead to a more targeted approach for rehabilitation interventions aiming to improve frailty in MS.

Modifying diet and exercise in multiple sclerosis (MoDEMS): A randomized controlled trial for behavioral weight loss in adults with multiple sclerosis and obesity.

BACKGROUND: Obesity is a risk factor for developing multiple sclerosis (MS) and MS-related disability. The efficacy of behavioral weight loss interventions among people with MS (pwMS) remains largely unknown. OBJECTIVE: Examine whether a group-based telehealth weight loss intervention produces clinically significant weight loss in pwMS and obesity. METHODS: Seventy-one pwMS were randomized to the weight loss intervention or treatment-as-usual (TAU). The 6-month program promoted established guidelines for calorie reduction and increased physical activity. Anthropometric measurements, mobility tasks, self-report questionnaires, and accelerometry were used to assess changes at follow-up. RESULTS: Mean percent weight loss in the treatment group was 8.6% compared to 0.7% in the TAU group (p < .001). Sixty-five percent of participants in the intervention achieved clinically meaningful weight loss (⩾ 5%). Participants in the treatment group engaged in 46.2 minutes/week more moderate-to-vigorous physical activity than TAU participants (p = .017) and showed improvements in quality of life (p = .012). Weight loss was associated with improved mobility (p = .003) and reduced fatiguability (p = .008). CONCLUSION: Findings demonstrate the efficacy of a behavioral intervention for pwMS and obesity, with clinically significant weight loss for two-thirds of participants in the treatment condition. Weight loss may also lead to improved mobility and quality of life.

Criterion validity of wrist accelerometry for assessing energy intake via the intake-balance technique.

BACKGROUND: Intake-balance assessments measure energy intake (EI) by summing energy expenditure (EE) with concurrent change in energy storage (ΔES). Prior work has not examined the validity of such calculations when EE is estimated via open-source techniques for research-grade accelerometry devices. The purpose of this study was to test the criterion validity of accelerometry-based intake-balance methods for a wrist-worn ActiGraph device. METHODS: Healthy adults (n = 24) completed two 14-day measurement periods while wearing an ActiGraph accelerometer on the non-dominant wrist. During each period, criterion values of EI were determined based on ΔES measured by dual X-ray absorptiometry and EE measured by doubly labeled water. A total of 11 prediction methods were tested, 8 derived from the accelerometer and 3 from non-accelerometry methods (e.g., diet recall; included for comparison). Group-level validity was assessed through mean bias, while individual-level validity was assessed through mean absolute error, mean absolute percentage error, and Bland-Altman analysis. RESULTS: Mean bias for the three best accelerometry-based methods ranged from -167 to 124 kcal/day, versus -104 to 134 kcal/day for the non-accelerometry-based methods. The same three accelerometry-based methods had mean absolute error of 323-362 kcal/day and mean absolute percentage error of 18.1-19.3%, versus 353-464 kcal/day and 19.5-24.4% for the non-accelerometry-based methods. All 11 methods demonstrated systematic bias in the Bland-Altman analysis. CONCLUSIONS: Accelerometry-based intake-balance methods have promise for advancing EI assessment, but ongoing refinement is necessary. We provide an R package to facilitate implementation and refinement of accelerometry-based methods in future research (see paulhibbing.com/IntakeBalance).

Sedentary Bout Patterns and Metabolic Health in the Hispanic Community Health Study/Study of Latino Youth (SOL Youth).

Background There is limited evidence on the potential negative metabolic health impacts of prolonged and uninterrupted sedentary bouts in structurally disadvantaged youth. This study investigated associations between sedentary bout variables and metabolic health markers in the Hispanic Community Health Study/SOL Youth (Study of Latino Youth). Methods and Results SOL Youth was a population-based cohort of 1466 youth (age range, 8-16 years; 48.5% female); 957 youth were included in the analytic sample based on complete data. Accelerometers measured moderate-to-vigorous physical activity (MVPA), total sedentary time, and sedentary bout patterns (daily time spent in sedentary bouts ≥30 minutes, median sedentary bout duration, and number of daily breaks from sedentary time). Clinical measures included body mass index, waist circumference, fasting glucose, glycated hemoglobin, fasting insulin, and the homeostasis model assessment of insulin resistance. After adjusting for sociodemographics, total sedentary time, and MVPA, longer median bout durations and fewer sedentary breaks were associated with a greater body mass index percentile (bbouts=0.09 and bbreaks=-0.18), waist circumference (bbouts=0.12 and bbreaks=-0.20), and fasting insulin (bbouts=0.09 and bbreaks=-0.21). Fewer breaks were also associated with a greater homeostasis model assessment of insulin resistance (b=-0.21). More time in bouts lasting ≥30 minutes was associated with a greater fasting glucose (b=0.18) and glycated hemoglobin (b=0.19). Conclusions Greater accumulation of sedentary time in prolonged and uninterrupted bouts had adverse associations with adiposity and glycemic control over and above total sedentary time and MVPA. Findings suggest interventions in Hispanic/Latino youth targeting both ends of the activity spectrum (more MVPA and less prolonged/uninterrupted sedentary patterns) may provide greater health benefits than those targeting only MVPA.

Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE).

BACKGROUND/OBJECTIVES: Sedentary behavior (SB) has both movement and postural components, but most SB research has only assessed low movement, especially in children. The purpose of this study was to compare estimates and health associations of SB when derived from a standard accelerometer cut-point, a novel sitting detection technique (CNN Hip Accelerometer Posture for Children; CHAP-Child), and both combined. METHODS: Data were from the International Study of Childhood Obesity, Lifestyle, and the Environment (ISCOLE). Participants were 6103 children (mean ± SD age 10.4 ± 0.56 years) from 12 countries who wore an ActiGraph GT3X+ accelerometer on the right hip for approximately one week. We calculated SB time, mean SB bout duration, and SB breaks using a cut-point (SBmovement), CHAP-Child (SBposture), and both methods combined (SBcombined). Mixed effects regression was used to test associations of SB variables with pediatric obesity variables (waist circumference, body fat percentage, and body mass index z-score). RESULTS: After adjusting for MVPA, SBposture showed several significant obesity associations favoring lower mean SB bout duration (b = 0.251-0.449; all p < 0.001) and higher SB breaks (b = -0.005--0.052; all p < 0.001). Lower total SB was unexpectedly related to greater obesity (b = -0.077--0.649; p from <0.001-0.02). For mean SB bout duration and SB breaks, more associations were observed for SBposture (n = 5) than for SBmovement (n = 3) or SBcombined (n = 1), and tended to have larger magnitude as well. CONCLUSIONS: Using traditional measures of low movement as a surrogate for SB may lead to underestimated or undetected adverse associations between SB and obesity. CHAP-Child allows assessment of sitting posture using hip-worn accelerometers. Ongoing work is needed to understand how low movement and posture are related to one another, as well as their potential health implications.

Predicting energy intake with an accelerometer-based intake-balance method.

Nutritional interventions often rely on subjective assessments of energy intake (EI), but these are susceptible to measurement error. To introduce an accelerometer-based intake-balance method for assessing EI using data from a time-restricted eating (TRE) trial. Nineteen participants with overweight/obesity (25-63 years old; 16 females) completed a 12-week intervention (NCT03129581) in a control group (unrestricted feeding; n 8) or TRE group (n 11). At the start and end of the intervention, body composition was assessed by dual-energy X-ray absorptiometry (DXA) and daily energy expenditure (EE) was assessed for 2 weeks via wrist-worn accelerometer. EI was back-calculated as the sum of net energy storage (from DXA) and EE (from accelerometer). Accelerometer-derived EI estimates were compared against estimates from the body weight planner of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Mean EI for the control group declined by 138 and 435 kJ/day for the accelerometer and NIDDK methods, respectively (both P ≥ 0·38), v. 1255 and 1469 kJ/day, respectively, for the TRE group (both P < 0·01). At follow-up, the accelerometer and NIDDK methods showed excellent group-level agreement (mean bias of -297 kJ/day across arms; standard error of estimate 1054 kJ/day) but high variability at the individual level (limits of agreement from -2414 to +1824 kJ/day). The accelerometer-based intake-balance method showed plausible sensitivity to change, and EI estimates were biologically and behaviourally plausible. The method may be a viable alternative to self-report EI measures. Future studies should assess criterion validity using doubly labelled water.

CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children.

BACKGROUND: Hip-worn accelerometer cut-points have poor validity for assessing children's sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data. METHODS: Participants were 278, 8-11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration). RESULTS: Across participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL. CONCLUSIONS: There was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children.

Sedentary Profiles: A New Perspective on Accumulation Patterns in Sedentary Behavior.

PURPOSE: The purpose of this study is to introduce SB profiles (i.e., groups of similar SB accumulation patterns) as a new approach to pattern-focused SB research. METHODS: Data came from the National Health and Nutrition Examination Survey (2003-2006). The SB bouts were determined from hip-worn accelerometer data, and k-medoid clustering was used to define three SB profiles (interrupted, intermediate, and prolonged). These were used as the exposure in logistic regression models to examine odds ratios (OR) of having high (>20%) predicted-10-yr risk of cardiovascular disease (CVD). For comparison, the models were also fitted using standard metrics (i.e., tertiles of mean SB bout duration and daily total SB time). RESULTS: Those with interrupted profile (n = 1740) were typically sedentary ~32% of the time, in bouts ≤15 min. Those with intermediate profile (n = 2453) were typically sedentary ~47% of the time, in bouts ≤20 min. Those with prolonged profile (n = 1302) were typically sedentary ~61% of the time, frequently in bouts >20 min. The fully adjusted model showed that high CVD risk was more common for the intermediate (OR, 1.87) and prolonged (OR, 4.65) profiles than the interrupted profile (all P < 0.001). For standard metrics, OR ranged from 1.46 (middle tertile of daily total SB time) to 4.11 (upper tertile of mean bout duration) with all P < 0.002. CONCLUSIONS: The SB profiles represent a promising new avenue for understanding and combating adverse SB trends. The value of the approach was highlighted by the strong associations with CVD risk.

CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35.

Background: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35-99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results: Mean errors (activPAL - CHAP-Adult) and 95% limits of agreement were: sedentary time -10.5 (-63.0, 42.0) min/day, breaks in sedentary time 1.9 (-9.2, 12.9) breaks/day, mean bout duration -0.6 (-4.0, 2.7) min, usual bout duration -1.4 (-8.3, 5.4) min, alpha .00 (-.04, .04), and time in ≥30-min bouts -15.1 (-84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: -2.0% (4.0%), -4.7% (12.2%), 4.1% (11.6%), -4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson's correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. Conclusions: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.

Modifying Diet and Exercise in MS (MoDEMS): Study design and protocol for a telehealth weight loss intervention for adults with obesity & Multiple Sclerosis.

Weight loss improves overall health, and reduces inflammation, risk of stroke, heart attack, diabetes, certain cancers, and death among individuals with obesity. Weight loss also improves mobility, increases stamina, and elevates mood. Between 25 and 33% of people with Multiple Sclerosis (pwMS) have obesity. Multiple Sclerosis (MS) and obesity are independently associated with reduced mobility, increased fatigue, and depression. Most behavioral weight loss trials exclude individuals with neurologic disease. Consequently, few studies have examined the effects of weight loss on symptom presentation and health outcomes among pwMS and obesity. This is the first study examining the efficacy of a comprehensive behavioral weight loss intervention designed specifically for pwMS. The purpose of this study is to develop and assess the efficacy of a telehealth administered weight loss intervention tailored for pwMS. Additionally, we aim to determine if weight loss reduces physical and emotional symptoms in individuals with obesity and MS. We will enroll 70 pwMS in a wait-list crossover trial to examine the efficacy of our intervention. If successful, findings will help determine whether we can help participants lose clinically significant weight - and whether weight loss among pwMS and overweight/obesity reduces fatigue, and improves mobility, mood, and quality of life.

Temporal patterns of physical activity in successful weight loss maintainers.

BACKGROUND/OBJECTIVES: Individuals successful at weight loss maintenance engage in high amounts of physical activity (PA). Understanding how and when weight loss maintainers accumulate PA within a day and across the week may inform PA promotion strategies and recommendations for weight management. METHODS: We compared patterns of PA in a cohort of weight loss maintainers (WLM, n = 28, maintaining ≥13.6 kg weight loss for ≥1 year, BMI 23.6 ± 2.3 kg/m2), controls without obesity (NC, n = 30, BMI similar to current BMI of WLM, BMI 22.8 ± 1.9 kg/m2), and controls with overweight/obesity (OC, n = 26, BMI similar to pre-weight loss BMI of WLM, 33.6 ± 5.1 kg/m2). PA was assessed during 7 consecutive days using the activPALTM activity monitor. The following variables were quantified; sleep duration, sedentary time (SED), light-intensity PA (LPA), moderate-to-vigorous intensity PA (MVPA), and steps. Data were examined to determine differences in patterns of PA across the week and across the day using mixed effect models. RESULTS: Across the week, WLM engaged in ≥60 min of MVPA on 73% of days, significantly more than OC (36%, p < 0.001) and similar to NC (59%, p = 0.10). Across the day, WLM accumulated more MVPA in the morning (i.e., within 3 h of waking) compared to both NC and OC (p < 0.01). WLM engaged in significantly more MVPA accumulated in bouts ≥10 min compared to NC and OC (p < 0.05). Specifically, WLM engaged in more MVPA accumulated in bouts of ≥60 min compared to NC and OC (p < 0.05). CONCLUSIONS: WLM engage in high amounts of MVPA (≥60 min/d) on more days of the week, accumulate more MVPA in sustained bouts, and accumulate more MVPA in the morning compared to controls. Future research should investigate if these distinct patterns of PA help to promote weight loss maintenance.

The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study.

INTRODUCTION: Sitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method. METHODS: CHAP was developed on 709 older adults who wore an ActiGraph GT3X+ accelerometer on the hip, with ground-truth sit/stand labels derived from concurrently worn thigh-worn activPAL inclinometers for up to 7 d. The CHAP method was compared with traditional cut-point methods of sitting pattern classification as well as a previous machine-learned algorithm (two-level behavior classification). RESULTS: For minute-level sitting versus nonsitting classification, CHAP performed better (93% agreement with activPAL) than did other methods (74%-83% agreement). CHAP also outperformed other methods in its sensitivity to detecting sit-to-stand transitions: cut-point (73%), TLBC (26%), and CHAP (83%). CHAP's positive predictive value of capturing sit-to-stand transitions was also superior to other methods: cut-point (30%), TLBC (71%), and CHAP (83%). Day-level sitting pattern metrics, such as mean sitting bout duration, derived from CHAP did not differ significantly from activPAL, whereas other methods did: activPAL (15.4 min of mean sitting bout duration), CHAP (15.7 min), cut-point (9.4 min), and TLBC (49.4 min). CONCLUSION: CHAP was the most accurate method for classifying sit-to-stand transitions and sitting patterns from free-living hip-worn accelerometer data in older adults. This promotes enhanced analysis of older adult movement data, resulting in more accurate measures of sitting patterns and opening the door for large-scale cohort studies into the effects of sitting patterns on healthy aging outcomes.

Adding Family Digital Supports to Classroom-Based Physical Activity Interventions to Target In- and Out-of-School Activity: An Evaluation of the Stay Active Intervention during the COVID-19 Pandemic.

Remotely delivered interventions are promising for reaching large numbers of people, though few have targeted multiple levels of influence such as schools and families. This study evaluated two versions (arms) of a remotely delivered classroom-based physical activity (CBPA) intervention. One arm solely included remote CBPA; the other included remote CBPA and mobile health (mHealth) family supports. Six schools were randomized to CBPA or CBPA+Family. Both arms were remotely delivered for seven weeks. CBPA+Family added behavior change tools delivered via text messages and newsletters to caregiver/child dyads. Garmin devices measured moderate-to-vigorous activity (MVPA) in both arms and were used for goal setting/monitoring in the CBPA+Family arm (integrated with the text messages). Caregivers completed surveys evaluating intervention acceptability. 53 participants (CBPA n=35; CBPA+Family n=18; 9.7±0.7 years) were included. Increases in MVPA were similar between arms, showing a pre-post effect of the CBPA but no additional effect of family supports. MVPA was low at baseline and during the first 3 weeks (CBPA 7.5±3.1 minutes/day; CBPA+Family 7.9±2.7 minutes/day) and increased by Weeks 6-8 (CBPA 56.8±34.2 minutes/day; CBPA+Family 49.2±18.7 minutes/day). Approximately 90% of caregivers reported high satisfaction with the added family support content. CBPA+Family participants wore the Garmin later into the study period. Remote delivery of CBPA appears feasible and effective for supporting increases in children's MVPA. Adding family supports to school-based interventions appears acceptable and may support engagement, demonstrating promise for more multilevel/multi-setting interventions, though the multilevel intervention was not more effective than the single-level intervention in increasing children's MVPA.

Validity of a Global Positioning System-Based Algorithm and Consumer Wearables for Classifying Active Trips in Children and Adults.

Objective: To investigate the convergent validity of a global positioning system (GPS)-based and two consumer-based measures with trip logs for classifying pedestrian, cycling, and vehicle trips in children and adults. Methods: Participants (N = 34) wore a Qstarz GPS tracker, Fitbit Alta, and Garmin vivosmart 3 on multiple days and logged their outdoor pedestrian, cycling, and vehicle trips. Logged trips were compared with device-measured trips using the Personal Activity Location Measurement System (PALMS) GPS-based algorithms, Fitbit's SmartTrack, and Garmin's Move IQ. Trip- and day-level agreement were tested. Results: The PALMS identified and correctly classified the mode of 75.6%, 94.5%, and 96.9% of pedestrian, cycling, and vehicle trips (84.5% of active trips, F1 = 0.84 and 0.87) as compared with the log. Fitbit and Garmin identified and correctly classified the mode of 26.8% and 17.8% (22.6% of active trips, F1 = 0.40 and 0.30) and 46.3% and 43.8% (45.2% of active trips, F1 = 0.58 and 0.59) of pedestrian and cycling trips. Garmin was more prone to false positives (false trips not logged). Day-level agreement for PALMS and Garmin versus logs was favorable across trip modes, though PALMS performed best. Fitbit significantly underestimated daily cycling. Results were similar but slightly less favorable for children than adults. Conclusions: The PALMS showed good convergent validity in children and adults and were about 50% and 27% more accurate than Fitbit and Garmin (based on F1). Empirically-based recommendations for improving PALMS' pedestrian classification are provided. Since the consumer devices capture both indoor and outdoor walking/running and cycling, they are less appropriate for trip-based research.

Calibration of the Online Youth Activity Profile Assessment for School-Based Applications.

A balance between the feasibility and validity of measures is an important consideration for physical activity research - particularly in school-based research with youth. The present study extends previously tested calibration methods to develop and test new equations for an online version of the Youth Activity Profile (YAP) tool, a self-report tool designed for school applications. Data were collected across different regions and seasons to develop more robust, generalizable equations. The study involved a total of 717 youth from 33 schools (374 elementary (ages 9-11), 224 middle (ages 11-14), and 119 high school (ages 14-18)) in two different states in the U.S. Participants wore a Sensewear monitor for a full week and then completed the online YAP at school to report physical activity (PA) and sedentary behaviors (SB) in school and at home. Accelerometer data were processed using an R-based segmentation program to compute PA and SB levels. Quantile regression models were used with half of the sample to develop item-specific YAP calibration equations and these were cross validated with the remaining half of the sample. Computed values of Mean Absolute Percent Error (MAPE) ranged from 15-25% with slightly lower error observed for the middle school sample. The new equations had improved precision compared to the previous versions when tested on the same sample. The online version of the YAP provides an efficient and effective way to capture school level estimates of PA and SB in youth.

Youth Metabolic Equivalents Differ Depending on Operational Definitions.

Youth metabolic equivalents (METy) are sometimes operationally defined as multiples of predicted basal metabolic rate (METyBMR) and other times as multiples of measured resting metabolic rate (METyRMR). PURPOSE: This study aimed to examine the comparability of METyBMR and METyRMR. METHODS: Indirect calorimetry data (Cosmed K4b) were analyzed from two studies, with a total sample of 245 youth (125 male participants, 6-18 yr old, 37.4% overweight or obese). The Schofield equations were used to predict BMR, and K4b data from 30 min of supine rest were used to assess RMR. Participants performed structured physical activities (PA) of various intensities, and steady-state oxygen consumption was divided by predicted BMR and measured RMR to calculate METyBMR and METyRMR, respectively. Two-way (activity-METy calculation) analysis of variance was used to compare METyBMR and METyRMR (α = 0.05), with Bonferroni-corrected post hoc tests. Intensity classifications were also compared after encoding METyBMR and METyRMR as sedentary behavior (≤1.50 METy), light PA (1.51-2.99 METy), moderate PA (3.00-5.99 METy), or vigorous PA (≥6.00 METy). RESULTS: There was a significant interaction (F(30) = 3.6, P < 0.001), and METyBMR was significantly higher than METyRMR for 28 of 31 activities (P < 0.04), by 15.6% (watching television) to 23.1% (basketball). Intensity classifications were the same for both METy calculations in 69.0% of cases. CONCLUSIONS: METyBMR and METyRMR differ considerably. Greater consensus is needed regarding how metabolic equivalents should be operationally defined in youth, and in the meantime, careful distinction is necessary between METyBMR and METyRMR.

Modifying Accelerometer Cut-Points Affects Criterion Validity in Simulated Free-Living for Adolescents and Adults.

Purpose: To assess changes in criterion validity when modifying cut-points for use in different epoch lengths. Method: Simulated free-living data came from 42 adolescents (2-hr each) and 29 adults (6-hr each) wearing a hip-worn accelerometer and portable indirect calorimeter (Cosmed K4b2). K4b2 data were classified as sedentary behavior (SB), light physical activity (LPA), or moderate-to-vigorous physical activity (MVPA), and compared to estimates from accelerometer data processed with three youth and three adult cut-points in six epoch lengths (1, 5, 10, 15, 30, and 60-s). A cut-point of 100 counts per minute was used for all SB estimates. Results: For both adolescents and adults, SB estimates in all but 60-s epochs were significantly higher than the criterion, by 18.4%-78.4% (all p < .02). CPS had varied effects on youth LPA, ranging from favorable effects for one cut-point (1.9% underestimation in 1-s epochs, versus 40.2% overestimation in the originally-calibrated epoch length; p < .01 and p = .91, respectively) to unfavorable effects for another (41.8% underestimation in 1-s epochs, versus 9.8% underestimation in the originally-calibrated epoch length; p < .01 and p = .39, respectively). Adult LPA estimates in 30-s or 60-s epochs were closest to the criterion (within 5.2%-37.3%, p = .0001-0.49). Youth MVPA estimates in 60-s epochs were closest to the criterion (within 9.5%-53.2%, all p < .05), whereas adult MVPA estimates in 1-s epochs were closest to the criterion (within 6.6%-34.2%, p = .02-0.59). Conclusion: Cut-point modification is not universally beneficial, and thus it is not recommended.

Evaluating the Performance of Sensor-based Bout Detection Algorithms: The Transition Pairing Method.

Bout detection algorithms are used to segment data from wearable sensors, but it is challenging to assess segmentation correctness. PURPOSE: To present and demonstrate the Transition Pairing Method (TPM), a new method for evaluating the performance of bout detection algorithms. METHODS: The TPM compares predicted transitions to a criterion measure in terms of number and timing. A true positive is defined as a predicted transition that corresponds with one criterion transition in a mutually exclusive pair. The pairs are established using an extended Gale-Shapley algorithm, and the user specifies a maximum allowable within-pair time lag, above which pairs cannot be formed. Unpaired predictions and criteria are false positives and false negatives, respectively. The demonstration used raw acceleration data from 88 youth who wore ActiGraph GT9X monitors (right hip and non-dominant wrist) during simulated free-living. Youth Sojourn bout detection algorithms were applied (one for each attachment site), and the TPM was used to compare predicted bout transitions to the criterion measure (direct observation). Performance metrics were calculated for each participant, and hip-versus-wrist means were compared using paired T-tests (α = 0.05). RESULTS: When the maximum allowable lag was 1-s, both algorithms had recall <20% (2.4% difference from one another, p<0.01) and precision <10% (1.4% difference from one another, p<0.001). That is, >80% of criterion transitions were undetected, and >90% of predicted transitions were false positives. CONCLUSION: The TPM improves on conventional analyses by providing specific information about bout detection in a standardized way that applies to any bout detection algorithm.

Alternative Wear-time Estimation Methods Compared to Traditional Diary Logs for Wrist-Worn ActiGraph Accelerometers in Pregnant Women.

BACKGROUND: This study sought to compare three sensor-based wear-time estimation methods to conventional diaries for ActiGraph wGT3X-BT accelerometers worn on the non-dominant wrist in early pregnancy. METHODS: Pregnant women (n= 108) wore ActiGraph wGT3X-BT accelerometers for 7 days and recorded their device on and off times in a diary (criterion). Average daily wear-time estimates from the Troiano and Choi algorithms and the wGT3X-BT accelerometer wear sensor were compared against the diary. The Hibbing 2-regression model was used to estimate time spent in activity (during periods of device wear) for each method. Wear-time and time spent in activity were compared with multiple repeated measures ANOVAs. Bland Altman plots assessed agreement between methods. RESULTS: Compared to the diary [825.5 minutes (795.1, 856.0)], the Choi [843.0 (95% CI 812.6, 873.5)] and Troiano [839.1 (808.7, 869.6)] algorithms slightly overestimated wear-time, whereas the sensor [774.4 (743.9, 804.9)] underestimated it, although only the sensor differed significantly from the diary (P < .0001). Upon adjustment for average daily wear-time, there were no statistically significant differences between the wear-time methods in regards to minutes per day of moderate to vigorous physical activity (MVPA), vigorous PA, and moderate PA. Bland Altman plots indicated the Troiano and Choi algorithms were similar to the diary and within ≤ 0.5% of each other for wear-time and MVPA. CONCLUSIONS: The Choi or Troiano algorithms offer a valid and efficient alternative to diaries for the estimation daily wear-time in larger-scale studies of MVPA during pregnancy, and reduce burden for study participants and research staff.